How to Audit Your Analytics Events in 5 Steps

You are making product decisions based on analytics data. But when was the last time anyone checked whether that data is actually correct?

Most teams discover their analytics are broken reactively: a dashboard number stops making sense, a funnel shows a 0% conversion rate, or someone notices that iOS and Android report different totals for the same event. By the time you notice, the damage is already done. You have been making decisions on bad data for weeks or months.

An analytics event audit is the fix. It is a structured process for comparing what your tracking plan says should be happening against what is actually happening in your codebase and your data warehouse. Here is how to do it in five steps.

Step 1: Export your current event list

Before you can audit anything, you need a complete inventory. Pull every distinct event name that has fired in the last 30 days from your analytics platform or data warehouse.

In most tools, this is a single query:

• Amplitude: Go to Data > Events to see all active event names and their volumes.
• Mixpanel: Use the Events report or query SELECT DISTINCT event FROM events WHERE time > now() - 30d in JQL.
• Segment: Check the Schema tab in your source to see all events and properties flowing through.
• Data warehouse: Query your events table for distinct event names with counts and last-seen timestamps.

Export this to a spreadsheet or structured format. You want three columns: event name, event volume (last 30 days), and last fired timestamp. Sort by volume descending. Your most-fired events are where audit effort matters most.

Step 2: Compare against your tracking plan

Now take your event list and compare it to your tracking plan. You are looking for three things:

• Events in production but not in the plan. These are undocumented events. Someone shipped them without a corresponding tracking plan entry. They might be legitimate and just undocumented, or they might be experiments, debug events, or duplicates that should be removed.
• Events in the plan but not in production. These are coverage gaps. The plan says they should exist, but they are not firing. Either the implementation was never completed, it was removed during a refactor, or it is broken.
• Events that exist in both but do not match. The name is the same, but the properties differ. Maybe the plan says amount is a required float, but production data shows it arriving as a string on some platforms. These mismatches are the most dangerous because they look correct at a glance.

If you are doing this manually, it takes time. A codebase scanner can automate the comparison by analysing your source code directly and reporting which events from the plan are implemented, missing, or drifting.

Step 3: Check cross-platform consistency

If your product runs on multiple platforms (iOS, Android, web, backend), the same event should behave identically everywhere. In practice, it rarely does.

For each of your high-volume events, check:

• Name consistency. Is the event called exactly the same thing on every platform? Purchase Completed on web and PurchaseCompleted on iOS are not the same event in most analytics tools. Check your naming conventions.
• Property parity. Does every platform send the same set of properties? If web sends currency but Android does not, your cross-platform funnels will be skewed.
• Type consistency. Is user_id a string everywhere, or is it an integer on one platform? Is amount always a float, or does one platform send it as a formatted string like "$19.99"?
• Volume ratios. If your iOS and Android apps have roughly equal user bases, event volumes should be in the same ballpark. A 10x difference for the same event usually means one platform is broken or firing events in a loop.

Cross-platform inconsistency is the most common source of event drift. It compounds over time and quietly makes your analytics unreliable.

Step 4: Identify duplicates and dead events

Every mature analytics setup accumulates cruft. An audit is the time to clean it up.

Duplicates are events that track the same user action under different names. purchase_completed and order_placed both exist because two teams instrumented the same action independently, or because a naming convention changed and the old event was never removed. Identify pairs that overlap and decide which one is canonical.

Dead events are events that still fire but no longer serve a purpose. The feature they tracked was removed six months ago, but the tracking call was not. Or the event was created for a one-time experiment and never cleaned up. Dead events cost money (most analytics tools charge per event ingested) and create noise in your event catalogue.

Ghost events are events in your tracking plan that have not fired in 30+ days. Either the feature is rarely used (legitimate), the implementation is broken (bug), or the feature was removed and the plan was not updated (stale documentation). Investigate each one.

Mark duplicates and dead events as deprecated in your tracking plan. Do not delete them from the plan entirely. Keeping a record of deprecated events prevents someone from re-creating them later.

Step 5: Fix, validate, and prevent regression

The audit identified problems. Now fix them systematically.

Prioritize by impact. Start with the events your core metrics depend on. If your north star metric is built from subscription_started and that event has type mismatches across platforms, fix it first. A broken event that nobody queries can wait.

Update the tracking plan. For every issue found, update the tracking plan entry: fix property types, add missing required flags, document allowed enum values, mark deprecated events. The plan should reflect reality after the audit, not before.

Fix the code. For implementation bugs (wrong types, missing properties, inconsistent names), create tickets and fix them. If you use code generation from your tracking plan, regenerating the SDK after updating the plan will catch most type-level issues at compile time.

Prevent regression. An audit is only useful if the problems do not come back. The mechanisms that prevent regression:

• Type-safe generated code from the tracking plan, so property type mismatches are compile errors
• A codebase scanner in your CI pipeline that flags coverage gaps and schema mismatches on every pull request
• Volume alerts on your critical events so you know immediately when something breaks
• A quarterly re-audit cadence (schedule it now, or it will not happen)

How often should you audit?

A full audit once per quarter is the right cadence for most teams. It aligns with typical planning cycles and catches drift before it compounds.

Between full audits, lighter checks should be continuous:

• Every PR that touches analytics: review the tracking plan change alongside the code change
• Weekly: check volume alerts for anomalies on your top 10 events
• Monthly: run the codebase scanner and review coverage trends

The goal is not to make auditing a heavy process. It is to make small, continuous checks the default, so the quarterly audit becomes a confirmation rather than a discovery of months of accumulated problems.

The audit checklist

A condensed version you can use as a template:

• Export all active events from your analytics platform (last 30 days)
• Compare event list against your tracking plan for gaps in both directions
• Check property types and required flags match between plan and production
• Verify event names are consistent across all platforms
• Verify property values and types are consistent across all platforms
• Compare event volumes across platforms for anomalies
• Identify and mark duplicate events
• Identify and deprecate dead events (firing but no longer needed)
• Investigate ghost events (in plan but not firing)
• Update tracking plan to reflect findings
• Create tickets for implementation fixes, prioritized by metric impact
• Set up prevention: generated code, CI scanner, volume alerts
• Schedule next audit

Getting started

If you have never audited your analytics events, do not try to audit everything at once. Start with the 10 events your most important dashboards depend on. Audit those thoroughly, fix what you find, and expand from there.

If you want to automate the hardest parts of the audit, Ordaze’s codebase scanner can compare your tracking plan against your actual codebase across all platforms in a single command. Combined with a structured tracking plan registry and type-safe code generation, most of the issues an audit would find are prevented before they ship.

Start with Ordaze free and run your first scan today.